HIGHEST RATED MOOC

This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image. Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible. Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.

Syllabus

Session 1: Introduction To Tensorflow We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images.

Session 2: Training A Network W/ Tensorflow We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image.

Session 3: Unsupervised And Supervised Learning This session goes deep. We create deep neural networks capable of encoding a large dataset, and see how we can use this encoding to explore "latent" dimensions of a dataset or for generating entirely new content. We'll see what this means, how "autoencoders" can be built, and learn a lot of state-of-the-art extensions that make them incredibly powerful. We'll also learn about another type of model that performs discriminative learning and see how this can be used to predict labels of an image.

Session 4: Visualizing And Hallucinating Representations This sessions works with state of the art networks and sees how to understand what "representations" they learn. We'll see how this process actually allows us to perform some really fun visualizations including "Deep Dream" which can produce infinite generative fractals, or "Style Net" which allows us to combine the content of one image and the style of another to produce widely different painterly aesthetics automatically.

Session 5: Generative Models The last session offers a teaser into some of the future directions of generative modeling, including some state of the art models such as the "generative adversarial network", and its implementation within a "variational autoencoder", which allows for some of the best encodings and generative modeling of datasets that currently exist. We also see how to begin to model time, and give neural networks memory by creating "recurrent neural networks" and see how to use such networks to create entirely generative text.

MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.

How do I register?

To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

How do these MOOCs or free online courses work?

MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.

Very high quality materials and video. with interesting subject matter. I was looking more for a course introducing working with TensorFlow. I have a deep CS background but limited ML exposure/experience. The sessions started out with a very good balance between hands on development, explanation, and theory, but I f
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Very high quality materials and video. with interesting subject matter. I was looking more for a course introducing working with TensorFlow. I have a deep CS background but limited ML exposure/experience. The sessions started out with a very good balance between hands on development, explanation, and theory, but I felt the Tensor flow aspect of the explanation facet started going down the further into the sessions you got. Too many instances for: "here is some code to run but I don't have time to explain it". I really think you should have spent some time on TensorBoard if a good portion of the code you were walking through in Session 5 was there to integrate with it.

Fun and insightful combination of learning TensorFlow with example applications using neural networks for image analysis, visualisation plus generating text and music.
The course presents a minimal amount of theory, and has a hands-on approach. A typical session involves building and running a basic deep network for a
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The course presents a minimal amount of theory, and has a hands-on approach. A typical session involves building and running a basic deep network for a task using TensorFlow commands (in Python on Jupyter notebook), getting a feel for what that does, then a guided use of a more advanced model. Assignments start with a 90% complete notebook, with gaps to fill and parameters to adjust.

Christopher Kelly
is taking this course right now, spending 15 hours a week on it and found the course difficulty to be hard.

I have an undergraduate degree in computer science that didn't include any machine learning and I'm very new to most of the concepts presented in this course. I've spent a ton of time on the Khan Academy and Coursera and I'm blown away by the quality and professionalism of the content of this course. Highly recommended!

The instructor seems very active on the forums and even set up a slack for the course. It's been great, and the homework and notebooks are really easy to follow. So far it has really made me think and seems a lot more engaging than the Udacity or Coursera course. Can't wait to see where it goes!

This is a great course -- the approach is quite unique compared to other Deep Learning courses as it is geared, from the outset, to doing creative work: visualisations, image processing, and generation.
Funnily, this actually helps to understand the core underlying concepts even better than if it was just a "formal" c
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This is a great course -- the approach is quite unique compared to other Deep Learning courses as it is geared, from the outset, to doing creative work: visualisations, image processing, and generation.

Funnily, this actually helps to understand the core underlying concepts even better than if it was just a "formal" course on Deep Learning as one actually _sees_ and gets a deeper intuition of how training hones (or diverges, when things go wrong) over solutions, how different activation functions "look", and, doing generation, how the models "understand" what a face is, or what a word (or the sound of the word) is.

Parag, the teacher, is clearly very fluent on the material and can reconstruct, from the ground up, the fun things going on in the DL world, as opposed to just hooking into some "style transfer library somewhere".

One wishes, though, that he had more assistants to help update all the examples and tutorials and libraries he provides as the underlying libraries like TensorFlow evolve and break their old APIs.

One last thing, for me very important: the course is running in "Adaptive Mode", which means that one can truly take time to master each of the chapters before moving forward to the next concepts. This is a truly great thing that separates this course from others which might also have great content but for which you have to cram up the material just stay on board.

I think this course is excellent and inspiring. The teaching material is very good and the quality of the lessons is high. Just one note: it's not easy, if you are new to Machine Learning it will take a lot of time and CPU. But it will be worth it!

This is a very hard course! But this is also a very hard subject matter. It is amazing to see this course geared towards Creative applications. Very well produced course. Probably one of the best I have seen online

This course is really great to learn TensorFlow and manipulate advanced net architectures, like VAE/GAN. The explanations of Parag are great. The code provided for the assignments is just outstanding and is definitively valuable after the course. I need to point out that the community around the course is really active
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This course is really great to learn TensorFlow and manipulate advanced net architectures, like VAE/GAN. The explanations of Parag are great. The code provided for the assignments is just outstanding and is definitively valuable after the course. I need to point out that the community around the course is really active. I had the opportunity to discuss with real creative people, which I have highly appreciated.

This is the course I was looking forward to enrol in to. DNN Implementation in various environments. Very supportive forum plus detailed instructions and on screen examples. Hard to resist to start next session, but I must say, I am taking one step at a time and first practising whats being taught and then moving onto
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This is the course I was looking forward to enrol in to. DNN Implementation in various environments. Very supportive forum plus detailed instructions and on screen examples. Hard to resist to start next session, but I must say, I am taking one step at a time and first practising whats being taught and then moving onto to next session. Though you might be missing a smile on the face of such nice tutor, but never the less lost of love and respect.

Fantastic course, moves very fast, huge learning curve, non programmers might struggle, be prepared for a lot of reading, the instructor is 1 on 1 with every student in the forums, when you finish you will realize you have entered a new universe of possibilities.

Excellent course for anyone without a technical background interested in harnessing the power of Machine Learning for their creative projects. The material is very well organised building up on practical examples and comes with great support from Parag. It has been immensely helpful for my own work and I highly recommend it!

I started taking this course and the most amazing part is being able to see the student work in the gallery. I can't believe that machine learning can result in images like this! I hope I am smart enough to be able to accomplish this myself. I also see that there is now a Part II and Part III of this course! Lots to learn.

Best course to learn tensor flow. You need have a good understanding of NN theory before starting this class. Otherwise you will feel lost very soon. Do Andrew NG's Machine Learning till week 5 and get to this course to get a good grasp of material.

So far, it is clearly the best tensorflow course I have seen. The instructor is very competent, the course material is well-designed and extensive, the quality of the videos and the english pronounciation is perfect.

Excellent content. The course is very neatly designed and has been presented in an appropriate manner. The course gradually moves from the core foundations to the applications of tensorflow. Parag has done an excellent job in putting forth all advanced concepts. Highly Recommended !

Very technical, very informative, really good analysis of the deep learning approaches. The excercises after each lecture are very useful for gathering some hands-on experience. Can't recommend enough :)

I learned so much! It's a fascinating course and I'd recommend it to anyone curious about AI or what is possible with it. It's not an easy course especially towards the end but gives you plenty to think about.

I'm a webdeveloper with a good 15 years of experience, but unfortunately I didn't have a proper education in IT or computer science, although having a general interest in software development. That means, I know my way around producing proper code, but have a harder time following if Math, Statistics, CV and other subj
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I'm a webdeveloper with a good 15 years of experience, but unfortunately I didn't have a proper education in IT or computer science, although having a general interest in software development. That means, I know my way around producing proper code, but have a harder time following if Math, Statistics, CV and other subjects pop up (as they do here).

The title implies a focus on creative applications, which is a bit of an overstatement. I was expecting some Processing/openFrameworks kind of course. Instead the course goes with a quick pace through all the fundamentals of AI to give the student an understanding on how an AI can draw, produce text on its own or how Deep Dream works.

That made the course that much harder for me, since I lack the basics, and I struggled a bit with the exercises.

I still give the course five starts, because the video material and explanation is really well made, good to follow and I got a lot of insights. There is tons of material to work on and the Jupyter notebook are well made. I actually spun up an easy network with Keras after the course and was super excited to understand all the terms and how AI networks work at all.

I don't think, I could have had a better intro to AI. Thanks Parag Mital :)

Rates
is taking this course right now, spending 1 hours a week on it and found the course difficulty to be medium.

This course has decent content, but the pedagogy is pretty unpolished in my opinion. Parag doesn't spend enough time convincing the student WHY they should care about what they are doing or provide enough context of where this material is situated in the wider landscape of machine learning, art or design. He gets strai
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This course has decent content, but the pedagogy is pretty unpolished in my opinion. Parag doesn't spend enough time convincing the student WHY they should care about what they are doing or provide enough context of where this material is situated in the wider landscape of machine learning, art or design. He gets straight into the technical details of the tensor flow library without really explaining where the student is going. As a result I spent much of the time wondering what the point of each exercise was. Why does figuring out the (trivial) syntax for a snippet of python code from a 3rd party library help my art practice, or my understanding of machine learning? The net result is course material that is neither conceptual and high level or low level and technical, but a grey blob somewhere in the middle.

Am taking this course right now, not a easy one though!
I have experience with other online courses on Deep learning as well. What i can say is, Parag is one of the better teachers out there!
He doesn't assume you know anything by default, splits everything into small chunks of information and explains them in a st
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Am taking this course right now, not a easy one though!

I have experience with other online courses on Deep learning as well. What i can say is, Parag is one of the better teachers out there!

He doesn't assume you know anything by default, splits everything into small chunks of information and explains them in a step by step manner.

If you are willing to spend some quality time on this course you will definitely be rewarded !

Great concept and a really interesting application of AI - useful course to help fast track you into using AI for creative purposes. It provides inspirational projects that are beyond 'toy examples' and you are encouraged to explore and experiment to learn and better understand how they work. Recommend for anyone who h
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Great concept and a really interesting application of AI - useful course to help fast track you into using AI for creative purposes. It provides inspirational projects that are beyond 'toy examples' and you are encouraged to explore and experiment to learn and better understand how they work. Recommend for anyone who has experience with Deep Learning and wants to delve into the world of Creative AI.

I really enjoyed this course and Parag was steadily explaining all the concepts steadily from very basic and then building up advanced concepts. His coding while doing lecture is awesome and I have coded along with the video. Then also, I have completed his assignments and learned very much.

Zubrycki Igorpartially completed this course, spending 3 hours a week on it and found the course difficulty to be medium.

The course was very good and inspiring. It is obviously not for everyone as the focus in on the artistic use of deep learning, however, this type of application is fun and quite easy to experiment and learn in the process.